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 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## GeostatsPy: Variogram Calculation for Subsurface Data Analytics in Python \n",
    "\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "\n",
    "### Basic Variography in Python with GeostatsPy\n",
    "\n",
    "Here's a simple workflow with some basic variogram calcuation with irregularly sampled data. This should help you get started with variogram caculation in subsurface modeling.\n",
    "\n",
    "#### Spatial Continuity \n",
    "\n",
    "**Spatial Continuity** is the correlation between values over distance.\n",
    "\n",
    "* No spatial continuity – no correlation between values over distance, random values at each location in space regardless of separation distance.\n",
    "\n",
    "* Homogenous phenomenon have perfect spatial continuity, since all values as the same (or very similar) they are correlated. \n",
    "\n",
    "We need a statistic to quantify spatial continuity! A convenient method is the Semivariogram.\n",
    "\n",
    "#### The Semivariogram\n",
    "\n",
    "Function of difference over distance.\n",
    "\n",
    "* The expected (average) squared difference between values separated by a lag distance vector (distance and direction), $h$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\gamma(\\bf{h}) = \\frac{1}{2 N(\\bf{h})} \\sum^{N(\\bf{h})}_{\\alpha=1} (z(\\bf{u}_\\alpha) - z(\\bf{u}_\\alpha + \\bf{h}))^2  \n",
    "\\end{equation}\n",
    "\n",
    "where $z(\\bf{u}_\\alpha)$ and $z(\\bf{u}_\\alpha + \\bf{h})$ are the spatial sample values at tail and head locations of the lag vector respectively.\n",
    "\n",
    "* Calculated over a suite of lag distances to obtain a continuous function.\n",
    "\n",
    "* the $\\frac{1}{2}$ term converts a variogram into a semivariogram, but in practice the term variogram is used instead of semivariogram.\n",
    "* We prefer the semivariogram because it relates directly to the covariance function, $C_x(\\bf{h})$ and univariate variance, $\\sigma^2_x$:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "Note the correlogram is related to the covariance function as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\frac{C_x(\\bf{h})}{\\sigma^2_x}\n",
    "\\end{equation}\n",
    "\n",
    "The correlogram provides of function of the $\\bf{h}-\\bf{h}$ scatter plot correlation vs. lag offset $\\bf{h}$.  \n",
    "\n",
    "\\begin{equation}\n",
    "-1.0 \\le \\rho_x(\\bf{h}) \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "#### Variogram Observations\n",
    "\n",
    "The following are common observations for variograms that should assist with their practical use.\n",
    "\n",
    "##### Observation \\#1 - As distance increases, variability increase (in general).\n",
    "\n",
    "This is common since in general, over greater distance offsets, there is often more difference between the head and tail samples.\n",
    "\n",
    "In some cases, such as with spatial cyclicity of the hole effect variogram model the variogram may have negative slope over somelag distance intervals\n",
    "\n",
    "Negative slopes at lag distances greater than half the data extent are often caused by too few pairs for a reliable variogram calculation\n",
    "\n",
    "##### Observation \\#2 - Calculated with over all possible pairs separated by lag vector, $\\bf{𝐡}$.\n",
    "\n",
    "We scan through the entire data set, searching for all possible pair combinations with all other data.  We then calculate the variogram as one half the expectation of squared difference between all pairs.\n",
    "\n",
    "More pairs results in a more reliable measure.\n",
    "\n",
    "##### Observation \\#3 - Need to plot the sill to know the degree of correlation.\n",
    "\n",
    "**Sill** is the variance, $\\sigma^2_x$\n",
    "\n",
    "Given stationarity of the variance, $\\sigma^2_x$, and variogram $\\gamma(\\bf{h})$:\n",
    "\n",
    "we can define the covariance function:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "The covariance measure is a measure of similarity over distance (the mirror image of the variogram as shown by the equation above).\n",
    "\n",
    "Given a standardized distribution $\\sigma^2_x = 1.0$, the covariance, $C_x(\\bf{h})$, is equal to the correlogram, $\\rho_x(\\bf{h})$: \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "##### Observation \\#4 - The lag distance at which the variogram reaches the sill is know as the range.\n",
    "\n",
    "At the range, knowing the data value at the tail location provides no information about a value at the head location of the lag distance vector.\n",
    "\n",
    "##### Observation \\#5 - The nugget effect, a discontinuity at the origin\n",
    "\n",
    "Sometimes there is a discontinuity in the variogram at distances less than the minimum data spacing.  This is known as **nugget effect**.\n",
    "\n",
    "The ratio of nugget / sill, is known as relative nugget effect (%). Modeled as a discontinuity with no correlation structure that at lags, $h \\gt \\epsilon$, an infinitesimal lag distance, and perfect correlation at $\\bf{h} = 0$.\n",
    "Caution when including nuggect effect in the variogram model as measurement error, mixing populations cause apparent nugget effect\n",
    "\n",
    "This exercise demonstrates the semivariogram calculation with GeostatsPy. The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. sample from the simulation\n",
    "3. calculate and visualize experimental semivariograms\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data.csv at https://git.io/fh4gm.\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB                       # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                 # GSLIB methods convert to Python    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                               # to set current working directory \n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "import matplotlib.pyplot as plt                         # plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"c:/PGE383\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
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      "text/plain": [
       "       X      Y  Facies  Porosity       Perm           AI\n",
       "0  100.0  900.0     1.0  0.100187   1.363890  5110.699751\n",
       "1  100.0  800.0     0.0  0.107947  12.576845  4671.458560\n",
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       "4  100.0  500.0     0.0  0.102468   1.952264  3835.270322"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df = pd.read_csv(\"sample_data.csv\")                     # read a .csv file in as a DataFrame\n",
    "df = pd.read_csv(r\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data.csv\") # load data from Dr. Pyrcz's GitHub respository\n",
    "#print(df.iloc[0:5,:])                                  # display first 4 samples in the table as a preview\n",
    "df.head()                                               # we could also use this command for a table preview "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will work by-facies, that is separating sand and shale facies and working with them separately.  This command extracts the sand and shale 'Facies\" into new DataFrames for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "   index      X      Y  Facies  Porosity       Perm           AI\n",
       "0      0  100.0  900.0     1.0  0.100187   1.363890  5110.699751\n",
       "1      8  100.0  100.0     1.0  0.137453   5.727603  5823.241783\n",
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       "3     10  200.0  800.0     1.0  0.125984  10.675436  4292.700500\n",
       "4     18  300.0  900.0     1.0  0.111516  27.999817  4183.466773"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate Sample Data by-facies - Deepcopy\n",
    "df_sand = pd.DataFrame.copy(df[df['Facies'] == 1]).reset_index() # copy only 'Facies' = sand records\n",
    "df_shale = pd.DataFrame.copy(df[df['Facies'] == 0]).reset_index() # copy only 'Facies' = shale records\n",
    "df_sand.head()                                          # preview the sand only DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics for Tabular Data\n",
    "\n",
    "The table includes X and Y coordinates (meters), Facies 1 and 0 (1 is sandstone and 0 interbedded sand and mudstone), Porosity (fraction), and permeability as Perm (mDarcy). \n",
    "\n",
    "There are a lot of efficient methods to calculate summary statistics from tabular data in DataFrames. The 'describe' command provides count, mean, minimum, maximum, and quartiles all in a nice data table. We use transpose just to flip the table so that features are on the rows and the statistics are on the columns.\n",
    "\n",
    "Let's look at and compare the sumamry statistics for sand and shale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>50.000000</td>\n",
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       "      <td>100.000000</td>\n",
       "      <td>469.000000</td>\n",
       "      <td>509.000000</td>\n",
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       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>162.0</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>162.0</td>\n",
       "      <td>0.181001</td>\n",
       "      <td>0.037196</td>\n",
       "      <td>0.083842</td>\n",
       "      <td>0.155735</td>\n",
       "      <td>0.194823</td>\n",
       "      <td>0.207754</td>\n",
       "      <td>0.242298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>162.0</td>\n",
       "      <td>293.798999</td>\n",
       "      <td>399.989890</td>\n",
       "      <td>0.381032</td>\n",
       "      <td>31.996547</td>\n",
       "      <td>155.888123</td>\n",
       "      <td>385.177730</td>\n",
       "      <td>2642.999829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>162.0</td>\n",
       "      <td>3441.701689</td>\n",
       "      <td>969.924695</td>\n",
       "      <td>1844.166880</td>\n",
       "      <td>2748.296631</td>\n",
       "      <td>3179.596509</td>\n",
       "      <td>3998.567914</td>\n",
       "      <td>6197.834381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count         mean         std          min          25%  \\\n",
       "index     162.0   117.981481   59.058816     0.000000    75.250000   \n",
       "X         162.0   831.111111  238.857269    50.000000   800.000000   \n",
       "Y         162.0   526.987654  142.707797   100.000000   469.000000   \n",
       "Facies    162.0     1.000000    0.000000     1.000000     1.000000   \n",
       "Porosity  162.0     0.181001    0.037196     0.083842     0.155735   \n",
       "Perm      162.0   293.798999  399.989890     0.381032    31.996547   \n",
       "AI        162.0  3441.701689  969.924695  1844.166880  2748.296631   \n",
       "\n",
       "                  50%          75%          max  \n",
       "index      117.500000   157.750000   260.000000  \n",
       "X          945.000000   975.000000  1005.000000  \n",
       "Y          509.000000   549.000000   939.000000  \n",
       "Facies       1.000000     1.000000     1.000000  \n",
       "Porosity     0.194823     0.207754     0.242298  \n",
       "Perm       155.888123   385.177730  2642.999829  \n",
       "AI        3179.596509  3998.567914  6197.834381  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sand.describe().transpose()                          # summary table of sand only DataFrame statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <td>99.0</td>\n",
       "      <td>149.666667</td>\n",
       "      <td>93.588330</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>225.500000</td>\n",
       "      <td>259.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>99.0</td>\n",
       "      <td>300.444444</td>\n",
       "      <td>196.365820</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>970.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>99.0</td>\n",
       "      <td>425.111111</td>\n",
       "      <td>183.665784</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>376.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>989.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>99.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>99.0</td>\n",
       "      <td>0.100212</td>\n",
       "      <td>0.014483</td>\n",
       "      <td>0.058871</td>\n",
       "      <td>0.091902</td>\n",
       "      <td>0.101987</td>\n",
       "      <td>0.109846</td>\n",
       "      <td>0.141657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>99.0</td>\n",
       "      <td>3.568463</td>\n",
       "      <td>6.782476</td>\n",
       "      <td>0.033611</td>\n",
       "      <td>0.723788</td>\n",
       "      <td>1.530878</td>\n",
       "      <td>3.743835</td>\n",
       "      <td>52.500870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>99.0</td>\n",
       "      <td>5450.493543</td>\n",
       "      <td>728.871517</td>\n",
       "      <td>3595.586977</td>\n",
       "      <td>4897.828718</td>\n",
       "      <td>5570.604405</td>\n",
       "      <td>5951.816775</td>\n",
       "      <td>7881.898531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count         mean         std          min          25%  \\\n",
       "index      99.0   149.666667   93.588330     1.000000    41.500000   \n",
       "X          99.0   300.444444  196.365820    40.000000   201.000000   \n",
       "Y          99.0   425.111111  183.665784    29.000000   376.000000   \n",
       "Facies     99.0     0.000000    0.000000     0.000000     0.000000   \n",
       "Porosity   99.0     0.100212    0.014483     0.058871     0.091902   \n",
       "Perm       99.0     3.568463    6.782476     0.033611     0.723788   \n",
       "AI         99.0  5450.493543  728.871517  3595.586977  4897.828718   \n",
       "\n",
       "                  50%          75%          max  \n",
       "index      201.000000   225.500000   259.000000  \n",
       "X          231.000000   300.000000   970.000000  \n",
       "Y          416.000000   456.000000   989.000000  \n",
       "Facies       0.000000     0.000000     0.000000  \n",
       "Porosity     0.101987     0.109846     0.141657  \n",
       "Perm         1.530878     3.743835    52.500870  \n",
       "AI        5570.604405  5951.816775  7881.898531  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shale.describe().transpose()                         # summary table of shale only DataFrame statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The facies have strong differences in their summary statistics.\n",
    "\n",
    "Let's transform the data grouped overall both facies (sand and shale) and separated by facies to normal score values (Gaussian distributed with a mean of 0.0 and variance of 1.0). This is required for sequential Gaussian simulation (common target for our variogram models) and the Gaussian transform assists with outliers and provides more interpretable variograms. \n",
    "\n",
    "Let's look at the inputs for the GeostatsPy nscore program.  Note the output include an ndarray with the transformed values (in the same order as the input data in Dataframe 'df' and column 'vcol'), and the transformation table in original values and also in normal score values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.nscore(df, vcol, wcol=None, ismooth=False, dfsmooth=None, smcol=0, smwcol=0)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.nscore                                         # see the input parameters required by the nscore function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following command will transform the Porosity and Permeabilty to standard normal. Note: we perform the transform for porsity and permeability and for each for the cases of:\n",
    "\n",
    "1. all facies together\n",
    "2. sand facies only\n",
    "3. shale facies only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform to Gaussian by Facies\n",
    "df['NPor'], tvPor, tnsPor = geostats.nscore(df, 'Porosity') # nscore transform for all facies porosity \n",
    "df_sand['NPor'], tvPorSand, tnsPorSand = geostats.nscore(df_sand, 'Porosity')  # nscore transform for sand facies porosity \n",
    "df_shale['NPor'], tvPorShale, tnsPorShale = geostats.nscore(df_shale, 'Porosity')  # nscore transform for shale facies porosity\n",
    "df['NPerm'], tvPermSand, tnsPermSand = geostats.nscore(df, 'Perm')  # nscore transform for all facies permeability\n",
    "df_sand['NPerm'], tvPermSand, tnsPermSand = geostats.nscore(df_sand, 'Perm')  # nscore transform for sand facies permeability\n",
    "df_shale['NPerm'], tvPermShale, tnsPermShale = geostats.nscore(df_shale, 'Perm')  # nscore transform for shale facies permeability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at a previous of one of the DataFrames to make sure that we now have the normal score porosity and permeability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>NPor</th>\n",
       "      <th>NPerm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100187</td>\n",
       "      <td>1.363890</td>\n",
       "      <td>5110.699751</td>\n",
       "      <td>-2.158819</td>\n",
       "      <td>-2.158819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137453</td>\n",
       "      <td>5.727603</td>\n",
       "      <td>5823.241783</td>\n",
       "      <td>-0.817609</td>\n",
       "      <td>-1.468475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>200.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137062</td>\n",
       "      <td>14.771314</td>\n",
       "      <td>5621.146994</td>\n",
       "      <td>-0.839418</td>\n",
       "      <td>-0.955141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>200.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.125984</td>\n",
       "      <td>10.675436</td>\n",
       "      <td>4292.700500</td>\n",
       "      <td>-1.031153</td>\n",
       "      <td>-1.237102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>300.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.111516</td>\n",
       "      <td>27.999817</td>\n",
       "      <td>4183.466773</td>\n",
       "      <td>-1.621370</td>\n",
       "      <td>-0.694044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index      X      Y  Facies  Porosity       Perm           AI      NPor  \\\n",
       "0      0  100.0  900.0     1.0  0.100187   1.363890  5110.699751 -2.158819   \n",
       "1      8  100.0  100.0     1.0  0.137453   5.727603  5823.241783 -0.817609   \n",
       "2      9  200.0  900.0     1.0  0.137062  14.771314  5621.146994 -0.839418   \n",
       "3     10  200.0  800.0     1.0  0.125984  10.675436  4292.700500 -1.031153   \n",
       "4     18  300.0  900.0     1.0  0.111516  27.999817  4183.466773 -1.621370   \n",
       "\n",
       "      NPerm  \n",
       "0 -2.158819  \n",
       "1 -1.468475  \n",
       "2 -0.955141  \n",
       "3 -1.237102  \n",
       "4 -0.694044  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sand.head()                                          # preview sand DataFrame with nscore transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks good! One way to check is to see if the relative magnitudes of the normal score transformed values match the original values.  e.g. that the normal score transform of 0.10 porosity normal score is less than the normal score transform of 0.14 porsity.  Also, the normal score transform of values close to the mean value should be close to 0.0 \n",
    "\n",
    "Let's also check the original and transformed sand and shale porosity distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)                                        # plot original sand and shale porosity histograms\n",
    "plt.hist(df_sand['Porosity'], facecolor='red',bins=np.linspace(0.0,0.4,50),alpha=0.2,density=True,edgecolor='black',label='Sand')\n",
    "plt.hist(df_shale['Porosity'], facecolor='blue',bins=np.linspace(0.0,0.4,50),alpha=0.2,density=True,edgecolor='black',label = 'Shale')\n",
    "plt.xlim([0.05,0.25]); plt.ylim([0,35.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Porosity Sand and Shale')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(122)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df_sand['NPor'], facecolor='red',bins=np.linspace(-3.0,3.0,40),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=False,edgecolor='black',label='Sand')\n",
    "plt.hist(df_shale['NPor'], facecolor='blue',bins=np.linspace(-3.0,3.0,40),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=False,edgecolor='black',label='Shale')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,0.50])\n",
    "plt.xlabel('Nscore Porosity'); plt.ylabel('Density'); plt.title('Nscore Porosity Sand and Shale')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normal score transform has correctly transformed the porosity over sand and shale facies to standard normal.  Let's plot the location maps of normal score transforms of porosity and permeability for all facies, sand facies and shale facies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.plasma                    # color map\n",
    "plt.subplot(231)\n",
    "GSLIB.locmap_st(df,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - All Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(232)\n",
    "GSLIB.locmap_st(df_sand,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - Sand Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(233)\n",
    "GSLIB.locmap_st(df_shale,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - Shale Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(234)\n",
    "GSLIB.locmap_st(df,'X','Y','NPerm',0,1000,0,1000,-3,3,'Nscore Permeability - All Facies','X (m)','Y (m)','Nscore Permeability',cmap)\n",
    "\n",
    "plt.subplot(235)\n",
    "GSLIB.locmap_st(df_sand,'X','Y','NPerm',0,1000,0,1000,-3,3,'Nscore Permeability - Sand Facies','X (m)','Y (m)','Nscore Permeability',cmap)\n",
    "\n",
    "plt.subplot(236)\n",
    "GSLIB.locmap_st(df_shale,'X','Y','NPerm',0,1000,0,1000,-3,3,'Nscore Permeability - Shale Facies','X (m)','Y (m)','Nscore Permeability',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.8, wspace=0.4, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what the parameters are for the gamv, irregular data, experimental variogram calculation program."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.gamv(df, xcol, ycol, vcol, tmin, tmax, xlag, xltol, nlag, azm, atol, bandwh, isill)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.gamv                                           # see the input parameters required by the gamv function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the location maps to help determine good variogram calculation parameters.\n",
    "\n",
    "We are ready to calculate variogram! Let's calculate isotropic variograms for the transformed normal score porosity and permeability for sand, shale and mixed (without separating sand and shale).  Some information on the parameters that I chose:\n",
    "\n",
    "```p\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 100.0; lag_tol = 50.0; nlag = 7; bandh = 9999.9; azi = 0; atol = 90.0; isill = 1\n",
    "```\n",
    "* tmin, tmax are trimming limits - set to have no impact, no need to filter the data\n",
    "* lag_dist, lag_tol are the lag distance, lag tolerance - set based on the common data spacing (100m) and tolerance as 100% of lag distance for additonal smoothing\n",
    "* nlag is number of lags - set to extend just past 50 of the data extent\n",
    "* bandh is the horizontal band width - set to have no effect\n",
    "* azi is the azimuth -  it has not effect since we set atol, the azimuth tolerance, to 90.0\n",
    "* isill is a boolean to standardize the distribution to a variance of 1 - it has no effect since the nscore transform sets the variance to 1.0\n",
    "\n",
    "Let's try running these variograms and visualizing them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate Sample Data Isotropic Variograms\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 100.0; lag_tol = 100.0; nlag = 7; bandh = 9999.9; azi = 0; atol = 90.0; isill = 1\n",
    "\n",
    "lag, por_sand_gamma, por_sand_npair = geostats.gamv(df_sand,\"X\",\"Y\",\"NPor\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "lag, por_shale_gamma, por_shale_npair = geostats.gamv(df_shale,\"X\",\"Y\",\"NPor\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "lag, por_gamma, por_npair = geostats.gamv(df,\"X\",\"Y\",\"NPor\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "\n",
    "lag, perm_sand_gamma, perm_sand_npair = geostats.gamv(df_sand,\"X\",\"Y\",\"NPerm\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "lag, perm_shale_gamma, perm_shale_npair = geostats.gamv(df_shale,\"X\",\"Y\",\"NPerm\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "lag, perm_gamma, perm_npair = geostats.gamv(df,\"X\",\"Y\",\"NPerm\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(lag,por_gamma,'x',color = 'black',label = 'All')\n",
    "plt.plot(lag,por_sand_gamma,'x',color = 'orange',label = 'Sand')\n",
    "plt.plot(lag,por_shale_gamma,'x',color = 'brown',label = 'Shale')\n",
    "plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "plt.title('Isotropic NSCORE Porosity Variogram')\n",
    "plt.xlim([0,700])\n",
    "plt.ylim([0,1.8])\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(lag,perm_gamma,'x',color = 'black',label = 'All')\n",
    "plt.plot(lag,perm_sand_gamma,'x',color = 'orange',label = 'Sand')\n",
    "plt.plot(lag,perm_shale_gamma,'x',color = 'brown',label = 'Shale')\n",
    "plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "plt.title('Isotropic NSCORE Permeaiblity Variogram')\n",
    "plt.xlim([0,700])\n",
    "plt.ylim([0,1.8])\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The experiental variograms have some interesting features:\n",
    "\n",
    "* the range of the sand porosity and permeability is greater than the shale porosity and permeability range\n",
    "* although the shale short range experimental points may be nosy due to sparce shale data\n",
    "\n",
    "There is much more that you could try out, e.g. direction variograms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I hope you find this code and demonstration useful. I'm always happy to discuss geostatistics, statistical modeling, uncertainty modeling and machine learning,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "**Michael Pyrcz**, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "On Twitter I'm the **GeostatsGuy** and on YouTube my lectures are on the channel, **GeostatsGuy Lectures**."
   ]
  }
 ],
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